疫情和疫苗模型的普遍特征
Sourav Chowdhury1, Indrani Bose2, Suparna Roychowdhury1
1Postgraduate and Research Department of Physics, St. Xavier's College (Autonomous), 30 Mother Teresa Sarani, Kolkata-700016, West Bengal, India.
Physical review. E
|August 1, 2025
概括
这项研究分析了一种随机流行病模型,其中包括噪音诱导的过渡和疫苗接种效应. 它揭示了流行病动态和疫苗犹模型之间的普遍特征,符合COVID-19数据.
科学领域:
- 数学生物学 数学生物学
- 流行病学 流行病学
- 统计物理 统计物理
背景情况:
- 随机易感-感染-易感 (SIS) 流行病模型对于理解疾病动态至关重要.
- 环境干扰可以在流行病模型中引发重大转变.
- 疫苗犹是影响疫苗接种活动成功的一个关键因素.
研究的目的:
- 在一个随机的SIS流行病模型中研究噪音诱导的转换与移民.
- 分析疫苗接种对流行病在随机环境中传播的影响.
- 使用真实世界的数据开发和验证疫苗犹模型.
主要方法:
- 对分叉图和稳定状态概率分布的分析.
- 使用乘数噪声建模环境干扰.
- 应用Beta分布来模拟疫苗犹,并推导出基本的繁殖数分布.
主要成果:
- 该模型展示了带有单模和双模概率分布的噪声诱导过渡.
- 一个临界点过渡属于平均场Ising普遍性类.
- 拟议的疫苗犹模型准确地符合COVID-19疫苗接种数据.
结论:
- 随机流行病模型与其他动态系统 (包括人口遗传学模型) 有共同的普遍特征.
- 通过了解随机效应和疫苗犹,可以优化疫苗接种策略.
- 贝塔分布为分析疫苗犹及其流行病学影响提供了一个强大的框架.
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